Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 169, Issue -, Pages 579-592Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2017.10.005
Keywords
Model validation; Multivariate output; Principal component analysis; Area metric
Funding
- National Natural Science Foundation of China [NSFC 51505382]
- Natural Science Foundation of Shaanxi Province [2016JQ1034]
- Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University
Ask authors/readers for more resources
Traditional methods for model validation assessment mainly focus on validating a single response. However, for many applications joint predictions of the multiple responses are needed. It is thereby not sufficient to validate the individual responses separately, which ignores correlation among multiple responses. Validation assessment for multiple responses involves comparison with multiple experimental measurements, which makes it much more complicated than that for single response. With considering both the uncertainty and correlation of multiple responses, this paper presents a new method for validation assessment of models with multivariate output. The new method is based on principal component analysis and the concept of area metric. The method is innovative in that it can eliminate the redundant part of multiple responses while reserving their main variability information in the assessment process. This avoids directly comparing the joint distributions of computational and experimental responses. It not only can be used for validating multiple responses at a single validation site, but also is capable of dealing with the case where observations of multiple responses are collected at multiple validation sites. The new method is examined and compared with the existing u-pooling and t-pooling methods through numerical and engineering examples to illustrate its validity and potential benefits. (C) 2017 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available